Machine Learning for Drug Discovery /
Machine Learning for Drug Discovery is designed to suit the needs of graduate students, advanced undergraduates, chemists or biologists otherwise new to this research domain with minimal previous exposure to Machine Learning (ML) methods, or computational scientists with minimal exposure to medicina...
| Main Authors: | , , , |
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| Format: | software, multimedia |
| Language: | eng |
| Published: |
Northwest Washington, Washington : American Chemical Society,
2022
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| Subjects: | |
| Online Access: | https://pubs-acs-org.ezproxy.utm.my/doi/book/10.1021/acsinfocus.7e5017 |
| _version_ | 1817845707515625472 |
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| author | Melo, Marcelo C.R., author 652450 Maasch, Jacqueline R. M. A., author 652448 Fuente-Nunez, Cesar de la, author 652449 ACS Publications (Online service) 645714 |
| author_facet | Melo, Marcelo C.R., author 652450 Maasch, Jacqueline R. M. A., author 652448 Fuente-Nunez, Cesar de la, author 652449 ACS Publications (Online service) 645714 |
| author_sort | Melo, Marcelo C.R., author 652450 |
| collection | OCEAN |
| description | Machine Learning for Drug Discovery is designed to suit the needs of graduate students, advanced undergraduates, chemists or biologists otherwise new to this research domain with minimal previous exposure to Machine Learning (ML) methods, or computational scientists with minimal exposure to medicinal chemistry. The e-book covers basic algorithmic theory, data representation methods, and generative modeling at a high level. The authors spotlight antibiotic discovery as a case study in ML for drug development and discuss diverse applications in drug-likeness prediction, antimicrobial resistance, and areas for future inquiry. For a more dynamic learning experience, open-source code demonstrations in Python are included. |
| first_indexed | 2024-03-05T17:29:49Z |
| format | software, multimedia |
| id | KOHA-OAI-TEST:609770 |
| institution | Universiti Teknologi Malaysia - OCEAN |
| language | eng |
| last_indexed | 2024-12-08T04:39:22Z |
| publishDate | 2022 |
| publisher | Northwest Washington, Washington : American Chemical Society, |
| record_format | dspace |
| spelling | KOHA-OAI-TEST:6097702024-10-05T03:17:43ZMachine Learning for Drug Discovery / Melo, Marcelo C.R., author 652450 Maasch, Jacqueline R. M. A., author 652448 Fuente-Nunez, Cesar de la, author 652449 ACS Publications (Online service) 645714 software, multimediaNorthwest Washington, Washington : American Chemical Society,2022©2022engMachine Learning for Drug Discovery is designed to suit the needs of graduate students, advanced undergraduates, chemists or biologists otherwise new to this research domain with minimal previous exposure to Machine Learning (ML) methods, or computational scientists with minimal exposure to medicinal chemistry. The e-book covers basic algorithmic theory, data representation methods, and generative modeling at a high level. The authors spotlight antibiotic discovery as a case study in ML for drug development and discuss diverse applications in drug-likeness prediction, antimicrobial resistance, and areas for future inquiry. For a more dynamic learning experience, open-source code demonstrations in Python are included.Includes index.Chapter 1. Pursuing New Models and Molecules -- Chapter 2. Key Algorithms for Drug Discovery -- Chapter 3. Data Representation in Computational Chemistry -- Chapter 4. Drug-likeness Prediction -- Chapter 5. Antimicrobial Activity Prediction -- Chapter 6. Antimicrobial Resistance Prediction -- Chapter 7. Generative Deep Learning for Drug Discovery -- Chapter 8. Future DirectionsMachine Learning for Drug Discovery is designed to suit the needs of graduate students, advanced undergraduates, chemists or biologists otherwise new to this research domain with minimal previous exposure to Machine Learning (ML) methods, or computational scientists with minimal exposure to medicinal chemistry. The e-book covers basic algorithmic theory, data representation methods, and generative modeling at a high level. The authors spotlight antibiotic discovery as a case study in ML for drug development and discuss diverse applications in drug-likeness prediction, antimicrobial resistance, and areas for future inquiry. For a more dynamic learning experience, open-source code demonstrations in Python are included.AlgorithmsAntimicrobial agentsCluster chemistryDrug discoveryPharmaceuticalshttps://pubs-acs-org.ezproxy.utm.my/doi/book/10.1021/acsinfocus.7e5017URN:ISBN:9780841299238 |
| spellingShingle | Algorithms Antimicrobial agents Cluster chemistry Drug discovery Pharmaceuticals Melo, Marcelo C.R., author 652450 Maasch, Jacqueline R. M. A., author 652448 Fuente-Nunez, Cesar de la, author 652449 ACS Publications (Online service) 645714 Machine Learning for Drug Discovery / |
| title | Machine Learning for Drug Discovery / |
| title_full | Machine Learning for Drug Discovery / |
| title_fullStr | Machine Learning for Drug Discovery / |
| title_full_unstemmed | Machine Learning for Drug Discovery / |
| title_short | Machine Learning for Drug Discovery / |
| title_sort | machine learning for drug discovery |
| topic | Algorithms Antimicrobial agents Cluster chemistry Drug discovery Pharmaceuticals |
| url | https://pubs-acs-org.ezproxy.utm.my/doi/book/10.1021/acsinfocus.7e5017 |
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